Cross-language analysis of world regions in the press

An empirical approach based on Wikidata

Claude Grasland & Etienne Toureille

19/10/2021

INTRODUCTION

Previous analysis on german (left) and french (right) newspapers has demonstrated the interest to analyse networks of states and world regions :

But before to validate this results we need :

  1. to clarify our definition of world regions and the associated list of target units.
  2. to enlarge the dictionary to other languages (turkish, arabic, english)

Looking for regions

The objective of this short note is to explore the possibility of Wikidata for the production of multilingual dictionaries of world regions and more generally regional imaginations. In order to test the interest of this approach, we will try to produce multilingual dictionaries for the identification of different types of “regions” related to the division of the Earth (“natural”) or the division of the World (“political”)

Earth/Natural regions: Atlas

So-called “Physical maps” in Atlas are a good source :

Earth/natural regions : Textbooks

Textbooks and educative games for children are also crucial :

World/Political regions : IGO

An attempt to classify intergovernmental organization in 4 types :

  1. Continental organizations
  2. Subcontinental organizations
  3. Transcontinental alliances
  4. Heritage of empires

Source : https://commons.wikimedia.org/wiki/Atlas_of_international_organizations

World/Political regions : Other …

A cross-language perspective

We propose to etablish a dictionary of Earth and World Regions in the five languages of interest for the project IMAGEUN :

We want to avoid any “eurocentric” or “anglocentric” perspective in the definition of entities. Therefore our definition of entities will follow the following rules :

  1. Non universal : Entities will not necessary be available in all languages
  2. Non equivalent : Translation of names does not imply equivalence of entities
  3. Non hierarchic : An entity has different definitions in each language. None of the language can be considered as “pivot” or “reference.”

Entities equivalences and lexical universes

To summarize, we propose to build partial equivalences between entities that belong to different lexical universes.

The comparison between lexical universes will be necessarily limited to a small sample of entities for which we can assume that the entities are approximately equivalent.

WIKIDATA

What is Wikidata ?

Wikidata defines itself as

Codification of entities

The first interest of wikidata is to provide unique code of identifications of objects. For example a research about “Africa” will produce a list of different objects characterized by a unique code :

Informations on entities

Once we have selected an entity (e.g. Q15) we obtain a new page with more detailed informations in english but also in all other languages available in Wikipedia.

Informations on entities

A lot of information are available concerning the entity but, at this stage, the most important ones for our research are :

  1. the translation in different languages
  2. the equivalent words or expression in different languages
  3. the definitions in different languages
  4. the ambiguity of the term in each language and the potential risks of confusion with other entities.

Of course we should not take for granted the answers proposed by wikidata (as noticed by Georg, Wikipedia is a matter of research for IMAGEUN …) but without any doubt, it offers a very good opportunity to clarify our questions and help us to build tools for recognition of world regions and other geographical imaginations in a multilingual perspective.

Multilanguage defintions

A wikipedia entity like Q15 is an element of an ontology designed by its author for specific purposes. The specificity of the wikidata ontology is the fact that it is a multilinligual web where Q15 is a node of the web present in different linguistic layers. It means that we don’t have a single name or a single definition of Q15, except if we adopt the neocolonial perspective to choose the english language as reference. Depending on the context (i.e. the language or sub-language), Q15 could be defined as :

language definition
fr A continent named Afrique
en A continent on the Earth’s northern and southern hemispheres named Africa or African continent
de A “Kontinent auf der Nord- und Südhalbkugel der Erde” named “Afrika”
tr A “Dünya nın kuzey ve güney yarıkürelerindeki bir kıta” named “Afrika” or “Afrika kıtası”
ar The second largest continent in the world in terms of area and population, comes second only to Asia (trad.)

Correspondance between entities ?

The existence of the same code of wikipedia entities does not offer any guarantee of concordance between the geographical objects found in news published in different languages or different countries. But - and it is the important point - it help us to point similarities and differences between set of geographical entities that are more or less comparable in each language.

Cross-language perspective

Having in mind the limits of the equivalence of entities across languages, it can nevertheless be an interesting experience to select a set of wikipedia entities (Q15, Q258, Q4412 …) and to examine their relative frequency in our different media from different countries with different languages. A typical hypothesis could be something like :

which is not equivalent to the question

but rather equivalent to the two joint questions

EXPERIMENT

We propose a semi-automatic method of extractions of entities in different languages that implies the presence of human expert at each step of the analysis. The figure below describe an example of research of world regions related to Africa in three languages.

The package WikidataR

The package WikidataR is an interface for the use of the Wikidata API in R language. Equivalent tools are available in Python and other languages for those non familiar with R. And it is of course possible to use directly the API. The first step is to install the most recent version of the R package WikidataR which install also related packages of interest.

#install.packages("WikidataR")
library(WikidataR)

Extraction of entities

If we start our research with the word “Afrique” in french language we find more than 50 entities that contain this word in their label. Only the first 10 are presented below :

item_id item_label item_desc item_lang item_text
Q15 Africa continent on the Earth’s northern and southern hemispheres fr Afrique
Q181238 Africa Roman province on the northern African coast covering parts of present-day Tunisia, Algeria, and Libya fr Afrique
Q203548 African Plate continental plate underlying Africa fr Afrique
Q258 South Africa sovereign state in Southern Africa fr Afrique du Sud
Q4412 West Africa region of Africa fr Afrique de l’Ouest
Q132959 Sub-Saharan Africa area of the continent of Africa that lies south of the Sahara Desert fr Afrique subsaharienne
Q27394 Southern Africa southernmost region of the African continent fr Afrique australe
Q27407 East Africa easterly region of the African continent fr Afrique de l’Est
Q27381 North Africa northernmost region of the African continent fr Afrique du Nord
Q2826196 Afrique Wikimedia disambiguation page fr Afrique

Selection of entities

The analysis of the list of result reveals four situations :

  1. Target entities: A first list is related to entities that can be considered as world regions or geographical imaginations of interest for IMAGEUN. It is typically the case for the whole continent of Afrique (Q15) and its different subdivisions like North Africa (Q27381), West Africa (Q4412), Sub-Saharan Africa (Q132959).

  2. Control entities : A list of entities that are not regions but should be controled if we want to identify our target entities. A typical example is the sovereign state of South Africa (Q258) which will necessary introduce mistakes in the identification of Africa as a continent if it is not controled. The problem will not necessary exist in all languages (e.g. German) but is important.

  3. Ambiguous entities : Some entities are ambiguous because they are not regions but use exactly the same textual units than a target entity. It is for example the case of the roman province of Africa (Q181238) which can not be easily differentiated from the continent, except by manual inspection. This units are not easy to control but fortunately are generally not frequent.

  4. Insignificant entities : Those entities that are exceptional inthe corpus can be simply gnored.

Bibliography

Cholley, André. 1939. “Régions naturelles et régions humaines.” L’information géographique 4 (2): 40–42. https://doi.org/10.3406/ingeo.1939.5013.